State-of-the-art DC–DC converters for electric mobility and renewable integration: trends, challenges, and future directions B. Nagi Reddy, B. Jyothi, Gundala Srinivasa Rao, N. Janaki, P. Swathi, Sareddy Venkata Rami Reddy Discover Applied Sciences, 2026 As electric vehicles and renewable energy systems become more widespread, there is an increasing need for DC–DC converters that can provide higher voltage gain, better efficiency, and improved reliability. This paper presents a comprehensive review of the latest advancements in enhanced gain DC–DC converter technologies. It covers a range of topologies including interleaved, coupled-inductor, switched-capacitor, multiport, and resonant converters, each suited for different application requirements. The review also explores the role of emerging semiconductor materials such as silicon carbide (SiC) and gallium nitride (GaN), as well as the integration of modern control strategies like model predictive control (MPC), fuzzy logic, and sliding mode control. Artificial intelligence (AI) and digital twin technologies are also discussed as tools for improving real-time performance and predictive maintenance. Through comparative studies and application-specific recommendations, this paper identifies key research gaps and future directions that could enhance scalability, cost-effectiveness, and thermal performance in power conversion systems for EVs and renewable energy.
An effective power quality enhancement system for integrated photovoltaic cells utilizing cascaded ANFIS in a unified power quality conditioner Saritha Kandukuri, Ramesh Guguloth, A. Sivakumar, I. Shivasankkar, Ananthan Nagarajan, N. Janaki Future Technology, 2025 The arrival of power electronic devices for the control of loads has an effect on the Power Quality (PQ) at the utility grid’s distribution side. Meanwhile, PQ problems cause malfunctioning equipment, lost production time, loss of money for industry, inconvenience, and possible damage to household electrical appliances. Thus, the requirement for increased system efficiency is essential. Hence, this study proposes the control of a Unified Power Quality Conditioner (UPQC) in conjunction with a Photovoltaic (PV) system. Shunt and series converters attached back-to-back via a shared DC-link make up the PV-UPQC system. Subsequently, the Artificial Neural Network (ANN) controller reduces PQ problems and simplifies the control complexity. A Coupled quadratic Single Ended Primary Inductor Converter (SEPIC) connects the PV system to UPQC, and the Cascaded Adaptive Neuro Fuzzy Inference System- Maximum Power Point Tracking (ANFIS-MPPT) technique enables the optimization of power extraction from PV sources. The developed approach is implemented using the MATLAB/Simulink platform, and its performance is evaluated for Total Harmonic Distortion (THD), sag, and swell. The results show that the control maintains THD within the B-phase THD of 3.97% and R and Y phase THDs of 4.82% and 4.86%, and also obtained a voltage gain ratio of 1:15; the output levels increase substantially with reduced voltage stresses on the switching devices.
A Non-Isolated Power Factor Correction Bridgeless High Gain Sepic Employing CPSO-PI Controller for Induction Motor Applications A.A. Mohamed Faizal, K. Murugesan, V. Thanka Jebarsan Ssrg International Journal of Electrical and Electronics Engineering, 2025 Induction Motors (IMs) are widely employed in different industrial applications owing to their robustness and dependability. However, their operation often poses challenges in terms of Power Factor Correction (PFC) and voltage regulation, leading to inefficient energy utilization and harmonic distortions. Traditional PFC methods and voltage regulation techniques may not adequately address these issues. To overcome these issues, this paper develops a novel approach leveraging a non-isolated Bridgeless High Gain Single-Ended Primary Inductance Converter (SEPIC) with Chaotic Particle Swarm Optimization (CPSO) based Proportional Integral (PI) controller approach for IM applications. The proposed converter configuration aims to enhance power conversion efficiency and improve power factor with reduced Total Harmonic Distortion (THD). Furthermore, a control strategy termed a PI controller is employed to optimize the converter control performance, and the CPSO algorithm is introduced to optimally tune the PI parameters to achieve the desired settling time and rapid convergence speed performance, thereby enhancing the overall Efficiency and performance of IM. Furthermore, the developed topology is validated by utilizing MATLAB/Simulink, and the developed converter and control technique are compared with the other recent approaches to prove the greatness of the proposed system. The investigational outcomes prove that the proposed converter has reduced THD (1.98%), and the control technique performed better in terms of settling time and convergence speed. The developed work demonstrates its applicability and superiority for IM applications in terms of power quality enhancement and energy efficiency with a better PFC system.
Artificial intelligence: Augmented integrated development environments for boosting programmer productivity P. Ashok, Ravi Gorli, S. Parameswari, Lakshmi Sridevi, N. Janaki, S. Gopinath, Harishchander Anandaram, K. S. Shreenidhi, Samaya Pillai Iyengar Artificial Intelligence for Cloud Native Software Engineering, 2025 AI is transforming software development with technologies that improve speed, quality, and productivity. AI-powered technologies and their use in software development are covered in this abstract. NLP algorithms help extract and categorize requirements from unstructured documents during requirements collecting and analysis. Machine learning algorithms forecast hazards and resource needs using past project data, improving planning and estimating. In addition, machine learning models trained on massive code repositories may produce code snippets and functions from natural language descriptions. AI algorithms produce test cases, prioritize test scenarios, and anticipate defect-prone code for testing and quality assurance. Automatic bug detection technologies use deep learning to spot bugs before they hit production. This research article brings in more insights about the various tools and softwares that are utilized in various stages of software development life cycle for efficient product development.
Quokka Swarm Driven PI Controller for High-Performance PV Microgrid with Interleaved Boost Converter N. Janaki, Arunagiri A, Mohudhoom Basith M, Vignesh G 2025 IEEE International Conference on Emerging Trends in Computing and Communication Etcom 2025, 2025 Over last decade, energy utilization based on Renewable Energy Source (RES) has grown significantly by more than 10 % each year. In addition to that, aiming at better overall grid efficiency and reliability, this study presents a new decentralized output-constrained control algorithm for Direct Current (DC) Micro Grids (MGs). It involves the study of PV integration in MG. The low voltage coming from the solar source is elevated by DC-DC converters. This paper presents an Interleaved Boost Converter (IBC) for raising the energy from the PV modules when they are in need of high voltage for an efficient DC microgrid operation. The suggested converter attaches PV modules as separate sources, provides high-efficiency and significant voltage conversion ratio with the minimal use of the components. In order to improve power quality and achieve higher system efficiency, the control strategy needs to be of great importance. QSO-optimized PI controllers are utilized to provide stable and dynamic power even during irradiance fluctuations. The proposed model for upgrading PV microgrid systems is implemented in MATLAB/Simulink and simulation results confirm that IBC converter reach an efficiency of 94.17 % with a higher gain than the traditional converters, simplified control mechanisms, and reduced energy losses with THD of 0.62%.
Automated Early Detection of Diabetes Mellitus from Retinal Fundus Images Using Residual U-Network Approach K. Sujatha, R.S. Ponmagal, N. Janaki, N.P.G. Bhavani, SuQun Cao Deep Learning in Diabetes Mellitus Detection and Diagnosis, 2025 Worldwide, diabetes mellitus (DM) is the consequential cause of death. The survivability of the patients is increased by early diagnosis of DM. Henceforth, it is very important to detect it as early as possible. To predict the segmented retinal images, many approaches have been proposed recently. Image-based analysis of the retinal fundus images is used as a non-invasive medical imaging modality. For medical image analysis, an image with elevated spatial resolution and contrast is required. Analysis of retinal images is the first step for early detection of DM. This goal is accomplished by segmentation of retinal fundus images using a residual U-network (RA-UNet) structure. The shuffled shepherd optimization algorithm (SSOA) and conditional autoregressive (CAViaR) algorithm facilitate in building an optimal architecture for deep learning neural network (DLNN) to detect and categorize diabetic retinopathy (DR) which is a consequence of DM. Random noise is eliminated using the adaptive threshold (AT) technique. Normal and abnormal regions of the retina are identified by the segmentation approach. Accurate delineation of the retina is carried out to segment and classify regions of the retina affected by DR. It is categorized as normal (NL), non-proliferative DR (NPDR), and proliferative DR (PDR). Fundus camera provides a better contrast for delineation of the blood vessels and hemorrhages, providing in-depth visibility. In the last few years, DLNN algorithms have exhibited prominent results in solving problems such as the detection and tracking of various retinal diseases by image classification and achieving promising results. Diagnosis of DM from retinal fundus images provides directions for quantitative analysis of research using DLNNs. Advanced communication technologies use the internet as a means of communication to connect the related devices called the internet of things (IoT), for the exchange of information. The uniquely identifiable objects are commonly referred to as IoT which are autonomous. These IoT devices can exchange digital information in the real world. IoT favors automation and offers flexibility and scalability in the design of healthcare systems with precision. Integration of infrastructure resources provides capability and effectiveness in healthcare IoT (HIoT) to share potential information among the users. Taking into consideration the remarkable breakthroughs made by these cutting-edge technologies, physicians have used relevant works based on imaging techniques, deep learning, and IoT to design an efficient algorithm for diagnosis by segmentation of retinal fundus images, emphasizing IoT simulation and routing, region of interest (RoI) extraction, residual attention-aware segmentation methods, and focusing on evaluation metrics such as the appropriateness measure. The research outcomes of this work can be transformed into a simulation package so that it can be used for medical diagnostics of NPDR and PDR at an early stage to save the life of the patient.
Evolutionary Computation in Early Detection and Classification of Plant Diseases from Aerial View of Agricultural lands K. Sujatha, R.S. Ponmagal, Prameeladevi Chillakuru, U. Jayalatsumi, N. Janaki, N.P.G. Bhavani Procedia Computer Science, 2025 This research presents a new combined deep learning system for effective and reliable identification of plant diseases in complicated agricultural environments. One of the most difficult jobs in agriculture is identifying plant diseases early on. Early disease detection in plants is crucial for increasing agricultural yield. With the application of machine learning and deep learning techniques, this issue has been resolved. Large crop farms can now detect plant illnesses automatically, which is advantageous as it reduces the monitoring time. The suggested approach consists of multiple important stages. To begin with, image quality of the agricultural lands is improved through preprocessing techniques like noise reduction, gamma correction and white balancing. Data augmentation is incorporated to expand the dataset and improve the generalization capacity of the model. Efficient methods such as EfficientDet and Squeeze Net, as well as color and shape based features, are included in feature extraction. The most relevant features are selected by a Hybrid Optimization Algorithm (HOA), which integrates Mother Optimization Algorithm (MOA), Teaching learning-based optimization (TLBO) and Improved Wild Horse Optimization to detect the various plant diseases like Bacterial Blight, Tungro, Blast and Brown spot. At last, a deep learning detector, which may include Recurrent Convolutional Neural Networks (R-CNNs) and Recurrent Neural Network (RNN), identifies the location and type of objects. The use of hyper parameter tuning techniques is also implemented to avoid over fitting and improve the overall generalization. This comprehensive approach depicts encouraging results in overcoming challenges in plant disease detection.
Cryptography using the Internet of Things T.R. Premila, N. Janaki, P. Govindasamy, E.N. Ganesh Advanced Technologies for Science and Engineering Volume 1 Intelligent Technologies for Automated Electronic Systems, 2024
Artificial Bee Colony Optimized Recurrent Neural Network-Based Port Container Throughput Forecast International Journal of Intelligent Systems and Applications in Engineering, 2023
High Switched Reluctance Generator for PSO Optimized WECS C. Fabbina, Riyaz A Rahiman, M. Prabha, N Janaki, M. Shadhik, Thanuja Penthala Proceedings of the International Conference on Circuit Power and Computing Technologies Iccpct 2023, 2023
